relationship between performance measurement …

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RELATIONSHIP BETWEEN PERFORMANCE MEASUREMENT SYSTEM ASPECTS AMONG AGRICULTURAL MARKETING CO-OPERATIVE

SOCIETIES IN ROMBO DISTRICT, TANZANIA

Victor Shirima Department of Banking, Accounting and Finance

Moshi Co-operative University - Tanzania

Henry Chalu Department of Accounting,

University of Dar es Salaam Business School, Dar es Salaam - Tanzania

Benson Ndiege Tanzania Co-operative Development Commission (TCDC), Dodoma-Tanzania

Email: [email protected]; [email protected]

Abstract Co-operative like any other institution has various performance measurement aspects that can be grouped into financial aspect and non-financial aspect. The non-financial aspect can be also subdivided into learning and growth, internal business process and members/customers. There has been a lot of theoretical claims on the causal relationship between performance measurement aspects. However, the empirical evidence on causal effect relationship between these aspects in co-operative sector has not got much attention. This paper, empirically, examined the causal relationships among performance measurement system aspects in Agricultural Marketing Co-operative Societies (AMCOS). Specifically, the paper examined the relationship between learning, business process, member and financial aspects. The study adopted a cross sectional design, where 334 respondents were involved. Structural Equation Model (SEM) was conducted to test the hypothesis. The findings revealed that: learning has a positive significant relationship with internal business process, internal business process with member aspect and member aspect with financial aspect. It also established a positive significant relationship between the learning aspect and financial aspect. The study concludes that, there is an empirical evidence on the presence significant relationship among co-operative performance aspects. The study recommends that, so long as learning and growth appear to influence all the other aspects, policy makers should put much efforts in developing human capital. Furthermore, it is recommended to those in charged with internal business processes to use the expertise and skills they have to come up with innovative ideas to provide quality services to members. Key word: Co-operative, Performance, Evaluation, Balanced Scorecard, Structural Equation Model Paper type: Research paper Type of Review: Peer Review

1. INTRODUCTION

Co-operative institutions are the popular economic institutions that have spread all over the world

(Ezekiel, 2014), to meet multiple goals of economic, social and cultural needs. Presence of multiple goals

East African Journal of Social and Applied Sciences (EAJ-SAS) Vol.2, No.2 Publication Date: October 20, 2020

ISSN: (Online) 2714-2051, (Print) 0856-9681

The current issue and full text archive of this journal is available at: http//www.mocu.ac.tz

Cite this article as: Shirima, V., Chalu, H. & Ndiege, O. B. (2020). Relationship between performance

measurement system aspects among agricultural marketing co-operative societies in Rombo district, Tanzania, East African Journal of Social and Applied Sciences, 2(2), 260-271.

mailto:[email protected]

mailto:[email protected]

Shirima, V., Chalu, H. & Ndiege, O. B. (2020). Relationship between performance measurement system aspects among agricultural

marketing co-operative societies in Rombo district, Tanzania.

The East African Journal of Social and Applied Sciences [EAJ-SAS] Vol.2, Issue 2, 2020 261

makes difficult and complex to evaluate the performance of these types of institutions. Given the nature

of the co-operatives, the performance should consider financial and non-financial aspects. Non-financial

aspect can also be subdivided into member aspect, internal business process aspect, learning aspect and

social aspect (Dhamayantie, 2018; Duguid, 2017; Shirima, Chalu, & Ndiege, 2019). The assumption

according to performance measurement system is the presence of causal effect relationship (Kaplan and

Norton, 1996, 2001). Causal relationship is very important in order to understand what to manage and the

priorities to be given during planning in various aspects. Knowing the causal relationship assists to

express how the indicators are linked together, hence then, can give a direction on what is supposed to

start with, during planning and the effect they have whenever the decision is made and therefore, used as

a strategic tool. Performance aspects and indicators have been discussed by a number of scholars in co-

operative (Dhamayantie, 2018; Masuku, Masuku, & Mutangira, 2016; Mayo, 2011), yet, the identification

of variables and their empirical causal effect relationship is not yet established.

Co-operative institutions are operating under a very competitive environment which needs a sound

understanding of what drives the performance. Resource Based View Theory assumes that organisation

can attain competitiveness by acquiring and possessing resources in their domains that are firm specific

and not available to competitors. These resources are human resources, technology, social relation

(Bloodgood, 2019; Campbell & Park, 2017). Nevertheless, a causal relationship assessment is very vital

within the performance measurement aspects. Cause-effect relations are also emphasised, by model like

Balanced Scorecard (BSC) system (Kaplan and Norton, 1996, 2001).

BSC was introduced by Kaplan and Norton in 1990s, and has been a framework for organisations to

translate their missions and strategies into a comprehensive set of key performance indicators. It is

composed of four perspectives in the following order: Learning and Growth, Internal Business Processes,

Customers (for the case of co-operatives members will be used), and Financial. Given the importance of

this model, the current study has also adopted the same to assess the causal relationship among various

aspects in AMCOS. The study used learning (competency, staff satisfaction, training and education,

number of employees, staff retention, employee skills); internal business process (quality of service and

quick service delivery, sufficient facilities, use of technology, new product development); member aspect

(member retention, member increase, member satisfaction, market share increase, member profitability);

and financial aspect (profitability, cost reduction, price, revenue growth, ROI, share increase). Although

some of the co-operatives might not know BSC (Cardemil & Shadbolt, 2006), still their organization’s

performance aspects fit well in the BSC framework. Cardemil and Shadbolt (2006) argued that, co-

operatives use all the BSC building blocks like objectives, measures and targets in four areas, namely

financial, customer, internal business process and learning and growth irrespectively of their relatively

small size and business field. The mentioned aspects form a holistic view of the institutional performance

system.

2. THEORETICAL PERSPECTIVE OF THE STUDY

Ability, Motivation and Opportunity to participate (AMO) theory argue that, for organisations to

achieve superior performance they need to ensure, they have human capital with appropriate skills,

abilities, motivated, and that they are given chance to execute their skills, knowledge and experience

(Marcoux, Guihur, & Leclerc, 2018; Moraes et al., 2018; Rajiani, Musa, & Hardjono, 2016). BSC model

insists the organisations to ensure learning and growth, internal business process, customers and

financial are monitored and assessed (Kaplan and Norton, 1996, 2001). The same applies to the AMCOS

which are required to struggle in order to increase AMCOS performance. In co-operatives members are

the ones who also approve the annual budget in the Annual General Meetings. Although they are not the

ones who perform the day to day activities, but their decisions for example cost reductions, might have




some impacts on the financial performance of the co-operative. Therefore, having members with skills,

experience and knowledge (learning aspect) on the co-operative issues can influence the financial Aspect.

Institutional performance system of the co-operative society considers four aspects, learning and growth,

internal business process, member and financial aspect. Learning and growth calls the continuous

improvement in organisation. Learning and growth is taken as a pillar for organisation’s success (Daniel,

2017) because, it tries to look on employee and members capabilities, information system capabilities and

on motivation, empowerment and alignment (Sainaghi, Phillips, & Corti, 2013). Therefore, learning

aspect when improved, it will lead to high-quality governance which is the most important factor to the

co-operative success. Human capital affects innovation and improves business operations. Therefore, it

affects the internal business process aspect (Chahal & Bakshi, 2015; Han & Li, 2015; Othman, Mansor, &

Kari, 2014). From the discussion above, two hypotheses were developed:

H1: Learning and growth aspect is positively associated with internal business process aspects

H2: Learning and growth aspect is positively associated with financial aspects

The internal business processes perspective is concerned with the operations of the organisations (Coe &

Letza, 2014; Hoque, 2014). Results from empirical studies show that intellectual capital elements (human,

innovation, process and customer aspect), directly affect business performance (Kalkan, Bozkurt, &

Arman, 2014; Wang & Chang, 2005). The study conducted by (Hejazi, Ghanbari, & Alipour, 2016) on the

profit-making organisations found that innovation capital affects process capital, which in turn influences

customer aspect which for this case is members. Therefore, the following hypothesis was developed:

H3: Internal business process aspect is positively associated with members’ aspect

Customer/member perspective identifies future wants and creating value in terms of time, quality and

service. It can be measured by market share, customer retention, acquisition, satisfaction and profitability

(Coe & Letza, 2014). Innovation capital affects process capital, which in turn influences customer capital.

Finally, customer capital contributes to financial performance (Hejazi et al., 2016). However, co-operatives

are as good as members make them (Borda & Vicari, 2014). It means the more you have good members

the more you have the good co-operative society. The following hypothesis is then developed:

H4: Members aspect is positively associated with financial aspects

The financial perspective examines whether the organisation’s strategy will contribute to the bottom-line

improvement of the organisation (Amaratunga & Baldry, 2002; Kaplan & Norton, 1996). The common

financial measures used in the financial perspective are revenue growth, costs, profit margins, cash flow,

net operating income assuming that AMCOS focus on the maximization of profit and net price (Royer,

2004).

H2 H4

H1

H3

Figure 1: Co-operative performance measurement framework

Business aspect

Learning and growth aspect Member

aspect

Financial aspect




3. METHODOLOGY

The study was conducted in Kilimanjaro where Rombo District was selected purposively because 100

percent (16 AMCOS) of the primary Agricultural Marketing Co-operatives Societies were active and were

doing business on their own with little dependency on the secondary co-operative which is Kilimanjaro

Native Co-operative Union (KNCU). Having being active and doing business by their own, it was

possible to have reliable information rather than using the co-operatives which are still using traditional

models by depending on KNCU in doing their business. The co-operative societies were also purposively

selected. The study design was a cross sectional design where survey approach was employed.

The sample size used in this study was 334 respondents obtained by using rule-of-thumb. The rule of

thumb technique was used because were no reliable statistics of the active members by using the criteria

of those selecting members who sold coffee through their co-operative for the last three years

consecutively. Therefore, neither finite population formula nor infinite population formula for sample

size could logically fit for these circumstances. Some researchers such as Hair et al, and Tatham, 1998,

Williams, Onsman, and Brown, 2010 suggest a rule of 10 variables per observation to be applied which

for this case is 10 times 22 indicators (220) or a rule of thumb of 100 participants and above. Generally, for

factor analysis a rule of thumb suggests having at least 300 sample size as adequate (Tabachnick, Fidell, &

Ullman, 2007; Van & Morgan, 2007).

A questionnaire was developed ensuring that no ambiguity in concepts and items by pre-testing and

respondents were informed about the anonymity as well as the confidentiality of the responses, in order

to emphasize honest answers. A total of 22 items: financial 6, member 5, internal 5, learning 6, were

assessed. Likert scale statements were developed ranging from 1 to 5 (ranging from 1→ strongly disagree;

2→ disagree; 3→ agree nor disagree; 4→ agree; 5→ strongly agree) to assess respondents’ perceptions on

how they agree the selected items to be used in their AMCOS as the important items for measuring their

performance. The study used members of the co-operative society as a unit of inquiry since scholars

recommend that, to understand the performance of the co-operative, ask members (Mayo, 2011). The

emphasis in co-operative society is on members’ value because these institutions are owned by members.

Active members depending on the criteria that, they have sold coffee through their co-operatives for the

past three years were chosen. Systematic sampling was involved where the first member was picked

randomly and then the others were picked using Kth formula depending on the list of members available

in a specific AMCOS.

Data were analysed through descriptive statistics for preliminary analysis, as well as, inferential statistics

for detailed analysis. Reliability using Cronbach Alpha was tested before proceeding with further steps.

The overall reliability of 0.939 which is above minimum required of 0.7 was achieved. Inferential statistics

was done stepwise: 1. Factor analysis using Principle Component Analysis (PCA) was conducted to

reduce redundant items and to increase the reliability of each aspect. According to Hair (2010), the

exploratory analysis procedure is a powerful tool that can address a wide range of theoretical questions,

hence allows the multivariate relationship. At this stage, the study was able to assess the convergent and

discriminant validity. 2. Thereafter, the Partial Least Square Structural Equation (PLS-SEM) by using

SMART-PLS 3.0 software was used in order to test the hypothesis in the model.

SEM was chosen because it tests multiple regression models in a single analysis at once and has become

popular technique to the researchers in social sciences (Hooper, Coughlan, & Mullen, 2008). PLS-SEM is

flexible to permit examination of complex associations and can handle various types of data (Wolf et al,

2013) and also combines factor analysis and linear regression (Hair et al, 2016). It also addresses the

problem of measurement error by removing it and therefore having a good estimation of relationship.




The PLS-SEM method was further used because it is designed as a prediction-oriented approach to SEM.

PLS-SEM path modelling using SMARTPLS is appropriate to carry on the confirmatory factor analysis

which is more reliable and valid (Afthanorhan, 2013) by combining principal components analysis with

regression-based path analysis. In avoiding the inherently problem of empirical social science data which

are characterised by non-normal data, PLS-SEM was suitable to solve this problem (Hair et al, 2014).

When using PLS-SEM analysis the researcher should pass through two stages (Chin, 2010) (1) the

assessment of the measurement model which includes the individual item reliability, internal

consistency, and discriminate validity of the measures, and (2) the assessment of the structural model.

3.1 Multicollinearity, Reliability and Validity Test

To assess the multicollinearity problem, variance inflation factor (VIF) was inspected. Table 1 indicates

that all VIF are below 10 as suggested by Chin (2010) meaning that multicollinearity problem does not

exist. Cronbach’s Alpha (Cronbach, 1951) is one of the widely used measures of reliability in the social

sciences (Bonett & Wright, 2015; Cronbach, 1951; Diedenhofen & Musch, 2016; Loewenthal & Lewis,

2018). Reliability of data was conducted in order to assess the internal consistency of the aspects through

Cronbach’s Alpha and was significant at an Alpha of 0.939 (see Table 1). Then, the aspects tested scored

the reliability above 0.7 which indicates a very strong consistency among aspects (Prajogo & Sohal, 2003).

The results gave a support to use factor analysis to determine whether some items could be removed and

to capture the meaning of the framework accurately. Bartlett’s test of sphericity and Kaiser- Meyer- Olkin

(KMO) measure of sampling adequacy were tested in order to evaluate the appropriateness of the data

for factor analysis. Bartlett’s test was significant at p < 0.001 level, indicating that there is association

among variables since the matrix is not identity matrix. Besides, the KMOs in Table 1 are higher than the

threshold of 0.5 (Darko et al., 2017; Williams, Onsman, & Brown, 2010), indicating that sample is

acceptable for factor analysis.

Factor Analysis was performed through principle component for the perspectives with a total of 22

items/indicators by using a principle component extraction and Varimax rotation. The eigen value for

each aspect was above 1.00. Financial aspect gave six indicators explaining a 54.584% of total variance

whereas member aspect has five indicators explaining a 60.911% of total variance. For the internal

business there are five indicators explaining a 52.262 % total variance whereas learning has six indicators

explaining 50.554% total variance. The total variance explained is within acceptable range of 50% for

social sciences. The entire factor loadings were above 0.50 which is acceptable (Hair et al, 2010), hence no

item was deleted at this stage.

Table 1: Testing for Multicollinearity and Reliability of data.

Aspect Cumulative

variance

Cronbach’s Alpha VIF KMO Bartlett’s Test

Financial 54.584% 0.861 1.499 0.894 P<0.001

Member 60.911% 0.839 1.750 0.854 P<0.001

Internal business 52.262% 0.847 1.655 0.875 P<0.001

Learning 50.554% 0.859 1.774 0.870 P<0.001

Overall reliability 0.939 NB: Determinants for the correlation matrices was >0.00001 indicating absence Multicolinearity.

Construct validity was measured in two aspects that are, convergent and discriminant validity. These

examine the extent to which measures of a latent variable shared their variance and how they are

different from others (Alarcón, Sánchez, & De Olavide, 2015). The Composite Reliability (CR) was used in

order to overcome some traditional CA’s deficiencies. The CRs in this study are in an acceptable range of




above 0.80. The last measure was a convergent validity to measure the degree to which individual items

reflects a perspective convergent in comparison to items measuring different aspects. Convergent validity

was achieved since the factor loadings were above 0.6 (see Table 2). This is a good threshold for

convergent validity (Park & Gagnon, 2006). The Average Variance Extracted (AVE) from this study as

recommended by Fornell and Larcker (1981), is above 0.5 indicating that convergent validity was

adhered.

Table 2: Factor loadings, Average Variance Extracted and Composite reliability

Construct Indicators Factor

loadings

VIF AVE Cronbach’s

alpha

Composite

Reliability

MA/CA CU1 0.781 1.747 0.611 0.841 0.841

CU2 0.795 1.851

CU3 0.815 1.942

CU4 0.732 1.539

CU5 0.783 1.673

FA F1 0.721 1.627 0.573 0.851 0.890

F2 0.767 1.773

F3 0.781 1.957

F4 0.758 1.709

F5 0.727 1.549

F7 0.786 1.879

IBPA I1 0.723 1.396 0.577 0.817 0.872

I2 0.710 1.543

I3 0.766 1.701

I4 0.774 1.758

I5 0.819 1.879

LGA L1 0.723 1.633 0.560 0.842 0.884

L2 0.812 2.127

L3 0.710 1.553

L4 0.774 1.860

L5 0.712 1.667

L6 0.754 1.802

MA: member aspect; FA: Financial Aspect: IBPA: internal business Aspect; LGA: learning Aspect: F1:

profitability; F2:price; F3: revenue growth; F4: return on investment; F5: share increase; F7: cost

reduction; L1: competency; L2: employee satisfaction; L3: employee retention; L4: training and education;

L5: employee skills; L6: number of employees; I1: quality of service; I2: Sufficient facilities; I3: use of

information technology: I4: new product development; I5: quick service delivery; CU1: member

satisfaction; CU2: member retention; CU3: market share increase; CU4: member profitability; CU5:

members increase

Discriminant Validity was tested according to Fornel and Larker (1981) criteria, that requires the square

root of AVE to be greater than the correlations among the constructs. All square root of AVE in Table 3

that appear in the diagonal for the model’s constructs are greater than the inter-construct correlations,

hence indicate that there is no problem with discriminant validity.




Table 3: Discriminant validity test for measurement model in PLS

FP IBP LG MP

FP 0.757

IBP 0.684 0.759

LG 0.556 0.721 0.748

MP 0.679 0.586 0.544 0.782

MA: member aspect; FA: Financial Aspect: IBA: internal business Aspect; LGA: learning Aspect:

4. FINDINGS

To assess the structural model, two measures namely: statistical significance (t- test) of the estimated path

coefficient- β, and the coefficient of determination - R2 (which explain the ability of the model to explain

the variance in the dependent variable. The hypothesis model was tested by using PLS method to confirm

the relationship between the constructs within the model. The paths in the model were tested to

determine their significance. Therefore, in order to assess the model, the squared multiple correlation (R2)

were examined in each construct. Then the significance of the paths was also evaluated.

Figure 2: The PLS-SEM results

R2 was assessed according to Chin (2010) who suggested that, values of approximately to 0.190 are weak,

values of 0.333 are moderate and 0.35 are substantial. Figure 2 shows that, 52.0% (R2) of the variance in

internal business is explained by learning aspect. 34.3% in member aspect is explained by internal

business while 51.1% of the financial aspect is explained by member aspect. All the R2 are substantial

according to Chin (2010).

Table 4: Partial least square results for model testing

Construct β T-value P Remarks

H3: IBP -> MP 0.588 12.669 0.000 Supported

H2: LG -> FP 0.267 5.591 0.000 Supported

H1: LG -> IBP 0.723 21.022 0.000 Supported

H4: MP -> FP 0.536 9.711 0.000 Supported

The significance test of the hypotheses, the t-value > 1.65 is significant at 0.05 level, and t-value >2 is

significant at 0.01 level (Teo et al, 2015; Sarstedt et al, 2014; Martinez & Aluja, 2009). Table 4 shows the

results of the structural model test where, all the hypotheses were supported at p < 0.01 significant level.




Therefore, it indicates that learning and growth aspect (β = 0.723, p < 0.01) has positive effect on internal

business operations; learning and growth aspect (β = 0.267, p < 0.01) on financial aspect; internal business

aspect (β = 0.588, p < 0.01) on member aspect; and, member aspect (β = 0.536, p < 0.01) on financial aspect.

Given the results from the hypotheses testing in Table 4, it shows that learning and growth in terms of

competence, satisfied employees, training and sufficient staff has a positive relationship with internal

business operations in terms of quality of services offered and quick service delivery. H2 was also

supported because results indicate that internal business aspect has a positive relationship with members’

aspect in terms of members’ satisfaction, members’ retention, market share increase and membership

increase. More over results show that member aspect has a positive relationship with financial aspect in

terms of co-operative profit, good price and cost reduction. However, it was found that learning has a

direct positive relationship with financial performance.

5. DISCUSSION

The findings show that when co-operatives have invested in learning and growth especially in terms of

competence, satisfied employees, training and sufficient staff there is a positive relationship with internal

business operations in terms of quality of services offered and quick service delivery. These will help the

co-operatives to achieve their visions, in order to sustain their abilities to change and improve over the

period. Learning and growth improves the efficiency in governance within the co-operative through

participation in economic and decision making. The result is supported by the study conducted by

Cardemil and Shadbolt (2006a) which found to have a causal linkage between attracting and retaining

quality staff resulting to good performance.

Competence in co-operatives should be in both members and employees. Members are the decision

makers during the AGM therefore, their full participation is vital for the co-operative performance

(Mahazril‘Aini, Hafizah, & Zuraini, 2012). However, participation without competence, knowledge and

skills is as good as not participating. Also, it is from members where the Board members are elected for a

period of three years. Therefore, having weak members can results to poor leaders. Furthermore,

competent employees will advise the board and do the day to day activities. Satisfied employees will

work hard while ensuring they offer quality services, hence good business process. Training is of

important through all levels from members, board members and employees. This leads to common

understanding in running the co-operative since by nature of this institutions, each level interacts in

running the institutions. However, staff should be sufficient depending on the size and functions of the

co-operative in order to perform the business operations and other activities within the co-operative. The

study is supported by Chareonwongsak (2017) who augmented that knowledge and skills influence good

operational performance.

The study also shows that internal business aspect is linked to members aspect. This is supported by

Hejaz et al. (2016) who found that innovation capital affects process capital, which in turn influences

customer capital. In business aspect the very important thing is the kind of business, the co-operative

shall excel so as to satisfy members (Vola, Broccardo, & Truant, 2009). Members want to get quality and

quick services. It is by having these services they will be satisfied and retained in their co-operative as

active members. Therefore, there is also a high possibility of continuing to sell the products through their

co-operative meaning that they are retained. Similarly, so long as there will be active participation due to

their satisfaction and continuing to sell products through their co-operatives, other prospective members

will be attracted to join the co-operative hence the possibility to increase market share within a certain

geographic area. Furthermore, members tend to increase when the services provided by the co-operative

society, meet the needs of the members.




Besides, when members aspect is performing properly as found in the study, there is statistical

significance relationship with financial aspect. Member aspect try to look on the way co-operative present

itself in front of members as the customers (Mahazril‘Aini et al, 2012; George and George, 2013; Vola,

2009). The same view has shown the causal linkage with the financial aspect which look on how the co-

operative will present itself to the members as the owner. The findings show that having the satisfied

members who are actively selling their products to their co-operatives can have a positive relationship

with financial aspect in terms of co-operative profit, good price and cost reduction. George and George

(2013) support the findings by arguing that, members trust and satisfaction influence the financial

performance of the co-operative. Good profit is because the turnover can increase due to increase of

members hence increase of volume to be sold. Also, can be the retention effect, meaning that the members

will channel all the products through the co-operative without selling to other buyers. But also, high price

can be obtained through bargaining power resulted from their large number due to increase and

participation. Cost reduction can be achieved due to members be loyal and doing some of the activities

voluntarily hence decreasing some unnecessary costs. However, it was found that investing in learning

aspect has a direct relationship with financial performance. This is due to some of the achievement in the

financial aspect requires competent and skilled people to deal with. Having those kinds of people will

support good bargaining power as well as dealing with financial management issues to reduce loss

within the co-operative.

6. CONCLUSION AND RECOMMENDATIONS

This study has provided an empirical evidence of the causal relationship between the co-operative

performance measurement aspects by adopting the BSC model which insists the causal relationship

within the aspects. The study found that learning and growth aspect (in terms of competency,

employee/staff satisfaction, retention, training and education, skills and number of employees), is the

base of the AMCOS and has a significance influence on both internal business process and financial

performance. It further found the significance influence of the internal business process (quality of

service, sufficient facilities, use of information technology, new product development, quick service

delivery) and the members aspects. Then it was also found that there is a significant influence of member

aspect (member satisfaction, member retention, market share increase, member profitability, members

increase) to the financial aspect. However, there were a direct causal linkage between the learning and

growth and the financial performance.

Since the findings shows the linkages in these aspects, the study recommends that so long as learning and

growth appear to influence all the other aspects, policy makers should put much efforts in developing

human capital through emphasizing trainings and employing skilled and competent staff. The priority

given in this aspect should not suppress the other aspects rather, others should be considered while

looking the capacity available in the learning and growth. The study is also recommending that members

should make sure, the training fund which is set aside according to the Co-operative Act and regulations,

should be utilised to all levels i.e. members, board members and staff so as to have competent persons in

all levels. Given that members need to receive services which meet their expectations, it is recommended

to those in charged with internal business processes to use the expertise and skills they have to come up

with innovative ideas to foster quality services to members. Also, since there is a significant association

between members and financial aspect, it is recommended to the members that each member should

make sure that he/she participate fully economically by selling the products through co-operative and

contribute to build capital.




REFERENCES

Afthanorhan, W. (2013). A comparison of partial least square structural equation modeling (PLS-SEM)

and covariance based structural equation modeling (CB-SEM) for confirmatory factor analysis.

International Journal of Engineering Science and Innovative Technology, 2(5): 198-205.

Alarcón, D., Sánchez, J. A., & De Olavide, U. (2015). Assessing convergent and discriminant validity in the

ADHD-R IV rating scale: User-written commands for Average Variance Extracted (AVE), Composite

Reliability (CR), and Heterotrait-Monotrait ratio of correlations (HTMT). Paper presented at the

Spanish STATA Meeting.

Amaratunga, D., & Baldry, D. (2002). Moving from performance measurement to performance

management. Facilities, 20(5/6): 217-223.

Bloodgood, J. M. (2019). Knowledge acquisition and firm competitiveness: the role of complements and

knowledge source. Journal of Knowledge Management, 23(1): 46-66.

Bonett, D. G., & Wright, T. A. (2015). Cronbach's alpha reliability: Interval estimation, hypothesis testing,

and sample size planning. Journal of Organizational Behavior, 36(1): 3-15.

Borda-Rodriguez, A., & Vicari, S. (2014). Rural co-operative resilience: the case of Malawi. Journal of Co-

operative Organization and Management, 2(1): 43-52.

Campbell, J. M., & Park, J. (2017). Extending the resource-based view: Effects of strategic orientation

toward community on small business performance. Journal of Retailing and Consumer Services, 34

(1), 302-308.

Cardemil-Katunaric, G., & Shadbolt, N. (2006). The Balanced Scorecard as a spontaneous framework in an

agricultural hybrid cooperative under strategic change: A case study in the New Zealand kiwifruit

industry. Paper presented at the World Food and Agribusiness Congress, Buenos Aires,

Argentina.

Chahal, H., & Bakshi, P. (2015). Examining intellectual capital and competitive advantage relationship:

role of innovation and organizational learning. International Journal of Bank Marketing, 33(3): 376-

399.

Chareonwongsak, K. (2017). Enhancing board motivation for competitive performance of Thailand’s co-

operatives. Journal of Co-operative Organization and Management, 5(1): 1-13.

Chin, W. W. (2010). How to write up and report PLS analyses. In Handbook of partial least squares (pp. 655-

690): Springer.

Coe, N., & Letza, S. (2014). Two decades of the balanced scorecard: A review of developments. The Poznan

University of Economics Review, 14(1): 63-75.

Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. psychometrika, 16(3), 297-334.

Daniel, K. K. (2017). Assessing the impact of co-operative education/training on co-operatives

performance. Journal of Strategy and Performance Management, 5(1): 23-41.

Dhamayantie, E. (2018). Designing a balanced scorecard for cooperatives. International Journal of

Organizational Innovation (Online), 11(2): 220-227.

Diedenhofen, B., & Musch, J. (2016). cocron: A Web Interface and R Package for the Statistical

Comparison of Cronbach's Alpha Coefficients. International Journal of Internet Science, 11(1): 51-60

Duguid, F. (2017). Non-financial tools and indicators for measuring the impact of co-operatives. Journal Of

Co-Operative Accounting And Reporting, 5(1): 40-54.

Ezekiel, P. O. (2014). A study on co-operative societies, poverty reduction and sustainable development in

Nigeria. IOSR Journal of Business Management, 6(1), 132-140.

F. Hair Jr, J., Sarstedt, M., Hopkins, L., & G. Kuppelwieser, V. (2014). Partial least squares structural

equation modeling (PLS-SEM) An emerging tool in business research. European Business Review,

26(2): 106-121.

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables

and measurement error. Journal of marketing research, 18(1): 39-50.




George, J., George, R., & Kulandaiswamy, V. (2013). Member trust’s impact on member satisfaction and

organizational performance: development of a conceptual model. Life Science Journal, 10(2): 604-

609.

Hair, J. F., Anderson, R. E., Babin, B. J., & Black, W. C. (2010). Multivariate data analysis: A global

perspective, ( 7). In: Upper Saddle River, NJ: Pearson.

Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (1998). Multivariate data analysis,

Prentice hall Upper Saddle River, NJ.pp 8

Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural

equation modeling (PLS-SEM): Sage publications.

Han, Y., & Li, D. (2015). Effects of intellectual capital on innovative performance: The role of knowledge-

based dynamic capability. Management Decision, 53(1): 40-56.

Hejazi, R., Ghanbari, M., & Alipour, M. (2016). Intellectual, human and structural capital effects on firm

performance as measured by Tobin's Q. Knowledge and Process Management, 23(4): 259-273.

Hooper, D., Coughlan, J., & Mullen, M. R. (2008). Structural equation modelling: Guidelines for

determining model fit. Electronic journal of business research methods, 6(1): 53-60.

Hoque, Z. (2014). 20 years of studies on the balanced scorecard: Trends, accomplishments, gaps and

opportunities for future research. The British accounting review, 46(1): 33-59.

Kalkan, A., Bozkurt, Ö. Ç., & Arman, M. (2014). The impacts of intellectual capital, innovation and

organizational strategy on firm performance. Procedia-Social and Behavioral Sciences, 150, 700-707.

Kaplan, R. S., & Norton, D. P. (1996). The balanced scorecard: translating strategy into action: Harvard

Business Press.pp 74

Loewenthal, K., & Lewis, C. A. (2018). An introduction to psychological tests and scales: Psychology press.

Mahazril‘Aini, Y., Hafizah, H., & Zuraini, Y. (2012). Factors affecting cooperatives’ performance in

relation to strategic planning and members’ participation. Procedia-Social and Behavioral Sciences,

65, 100-105.

Marcoux, G., Guihur, I., & Leclerc, A. (2018). Co-operative difference and organizational commitment:

The filter of socio-demographic variables. The International Journal of Human Resource Management,

1-24.

Martinez-Ruiz, A., & Aluja-Banet, T. (2009). Toward the definition of a structural equation model of

patent value: PLS path modelling with formative constructs. REVSTAT–Statistical Journal, 7(3):

265-290.

Masuku, T., Masuku, M., & Mutangira, J. (2016). Performance of Multi-Purpose Cooperatives in the

Shiselweni Region of Swaziland. International Journal of Sustainable Agricultural Research, 3(4), 58-

71.

Mayo, E. (2011). Co-operative performance. Sustainability Accounting, Management and Policy Journal, 2(1):

158-164.

Moraes, S. d. S., Chiappetta Jabbour, C. J., Battistelle, R. A., Rodrigues, J. M., Renwick, D. S., Foropon, C.,

& Roubaud, D. (2018). When knowledge management matters: interplay between green human

resources and eco-efficiency in the financial service industry. Journal of Knowledge Management.

Othman, A., Mansor, N., & Kari, F. (2014). Assessing the performance of co-operatives in Malaysia: an

analysis of co-operative groups using a data envelopment analysis approach. Asia Pacific Business

Review, 20(3): 484-505.

Park, J. A., & Gagnon, G. B. (2006). A causal relationship between the balanced scorecard perspectives.

Journal of Human Resources in Hospitality & Tourism, 5(2): 91-116.

Prajogo, D. I., & Sohal, A. S. (2003). The relationship between TQM practices, quality performance, and

innovation performance: An empirical examination. International journal of quality & reliability

management, 20(8): 901-918.




Rajiani, I., Musa, H., & Hardjono, B. (2016). Research Article Ability, Motivation and Opportunity as

Determinants of Green Human Resources Management Innovation.

Royer, J. S. (2004). Co-operative Theory: A Neoclassical Approach. Режим доступа: http://agecon. unl.

edu/royer/theory. pdf.

Sainaghi, R., Phillips, P., & Corti, V. (2013). Measuring hotel performance: Using a balanced scorecard

perspectives’ approach. International Journal of Hospitality Management, 34(1), 150-159.

Sarstedt, M., Ringle, C. M., Smith, D., Reams, R., & Hair Jr, J. F. (2014). Partial least squares structural

equation modeling (PLS-SEM): A useful tool for family business researchers. Journal of Family

Business Strategy, 5(1): 105-115.

Tabachnick, B. G., Fidell, L. S., & Ullman, J. B. (2007). Using multivariate statistics (Vol. 5): Pearson Boston,

MA.

Teo, A. C., Tan, G. W.-H., Ooi, K.-B., & Lin, B. (2015). Why consumers adopt mobile payment? A partial

least squares structural equation modelling (PLS-SEM) approach. International Journal of Mobile

Communications, 13(5): 478-497.

VanVoorhis, C. W., & Morgan, B. L. (2007). Understanding power and rules of thumb for determining

sample sizes. Tutorials in quantitative methods for psychology, 3(2): 43-50.

Vola, P., Broccardo, E., & Truant, E. (2009). Performance measurement under Balanced Scorecard: the case

study of a Co-operative Credit Bank in Piedmont. Economia Aziendale Online, 1(3): 131-152.

Wang, W. Y., & Chang, C. (2005). Intellectual capital and performance in causal models: evidence from

the information technology industry in Taiwan. Journal of Intellectual Capital, 6(2): 222-236.

Williams, B., Onsman, A., & Brown, T. (2010). Exploratory factor analysis: A five-step guide for novices.

Australasian Journal of Paramedicine, 8(3): 1-13

Wolf, E. J., Harrington, K. M., Clark, S. L., & Miller, M. W. (2013). Sample size requirements for structural

equation models: An evaluation of power, bias, and solution propriety. Educational and

psychological measurement, 73(6): 913-934.

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